Developing innovative analytics to estimate age-and cause-specific child mortality for low- and middle-income countries

项目摘要 2015年，全球估计有590万儿童在五岁生日之前死亡。大多数人死于 低收入和中等收入国家(低收入和中等收入国家)，提供关于特定年龄和具体原因的儿童死亡率的高质量信息 (ACSCM)很少可用。最近，美国政府和国际社会更新了 他们承诺在一代人的时间内结束可预防的儿童死亡。我们一直在出版全国模范 自2010年以来，我们估计了0-1个月和1-59个月婴儿的COD分布。 然而，人口统计学和流行病学证据得出的结论是，儿童的死亡人数并不一致。 在1-59个月期间。低于1-59个月的特异性水平的国家经验数据通常不是 由于民事登记系统薄弱，可在LMIC中使用。这些数据和估计具有相当大的科学性。 为特定年龄儿童干预措施的发展和影响评估提供信息的价值 放大。因此，了解1-59个月期间较细年龄组的COD分布是 这是正当的。以前的研究有四个主要缺陷：(I)使用定制收集的数据来 了解单一原因中的年龄动态；(Ii)仅在广泛的年龄组中估计ACSCM；(Iii)产生 每个年龄组单独和独立的估计；以及(Iv)在两个独立的年龄组中发展ACSCM 评估框架。这项研究的目标是系统地描述和公开可用 LMICs中儿童COD的经验年龄模式与准确的不确定区间，并开发创新 理论驱动、节俭的贝叶斯分层建模框架，以得出国家COD估算值 与以前的研究相比，部分数据的LMIC在更精细的年龄组中的分布。我们将实现 通过三个目标实现的目标：1)推广和评估所有年龄人口模型，以估计#年的年龄模式 使用虚拟现实数据的儿童死亡人数。2)概念化、开发和评估新的同时ACSCM估计器 使用高收入国家的VR数据；以及3)将统一的ACSCM估计框架外推至 LMIC。这项拟议的研究有两个重要的创新。首先，它提出了第一个统一的框架 同时估计所有年龄、所有原因、年龄和特定原因的儿童死亡率。如果成功，这项研究 将为选定的LMIC提供具有有效不确定度的系统评估ACSCM，并为 制定系统地评估所有LMIC的ACSCM的方法，包括那些低质量、有限或 甚至没有数据。其次，这个框架生成的估计值的年龄粒度在已发表的数据中还未见过 研究。这一补充信息对于五岁以下儿童生存政策的制定和 对年龄和原因进行粒度级别的计划评估，以进一步促进可持续发展 公平降低各国5岁以下儿童死亡率和新生儿死亡率的发展目标。本研究 将为未来的研究奠定基础，例如将人类死亡数据库扩展到以下儿童 五是对产品质量进行评估，调整偏差，并系统地组织ACSCM估计。

Project Summary Globally, an estimated 5.9 million children died before reaching their fifth birthday in 2015. The majority died in low- and middle-income countries (LMICs), where quality information on age- and cause-specific child mortality (ACSCM) is rarely available. Recently, the US government and the international community have renewed their commitment to end preventable child deaths in a generation. We have been publishing modeled national COD distributions for LMICs since 2010, where we estimated COD distribution for 0-1 and 1-59-month olds. However, demographic and epidemiological evidence amounts to the conclusion that child COD is not uniform in the 1-59-month period. National empirical data at levels of specificity below 1-59 months are often not available in LMICs due to weak civil registration systems. Such data and estimates bear considerable scientific value to inform the development and impact evaluation of age-specific childhood interventions and their scale-up. Therefore, understanding the COD distribution among finer age groups in the 1-59-month period is warranted. Previous research has suffered from four main drawbacks: (i) using custom-collected data to understand age dynamics in a single cause; (ii) estimating ACSCM only in broad age groups; (iii) producing estimates in each age group separately and independently; and (iv) developing ACSCM in two separate estimation frameworks. The goals of this study are to systematically describe and make publicly available empirical age patterns of child COD in LMICs with accurate uncertainty intervals, and to develop the innovative theory-driven, parsimonious Bayesian hierarchical modeling framework to derive estimates of national COD distributions in LMICs with partial data among finer age groups than previous research. We will achieve the goals through three aims: 1) To extend and evaluate all-age demographic models to estimate age patterns in child deaths with VR data. 2) To conceptualize, develop and evaluate novel simultaneous ACSCM estimators using VR data in high-income countries; and 3) To extrapolate the unified ACSCM estimation framework to LMICs. The proposed study has two important innovations. First, it proposes the first unified framework for simultaneously estimating all-age, all-cause, age- and cause-specific child mortality. If successful, the study will offer systematically estimated ACSCM with valid uncertainty for selected LMICs, and lay the foundation for developing methods to systematically estimate ACSCM for all LMICs, including those low quality, limited, or even no data. Second, this framework produces estimates at an age granularity not yet seen in published research. This additional information is crucial to enable under-five child survival policy development and program evaluation at granular levels of ages and causes that would further contribute to the Sustainable Development Goals of equitably in reducing under-5 and neonatal mortality rates across countries. This study will lay the groundwork for future research, such as extending the Human Mortality Database to children under five to produce quality assessed, bias adjusted, and systematically organized ACSCM estimates.